{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6bc7b9d8",
   "metadata": {},
   "source": [
    "## Navigating options market data with the OpenBB Platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "097d7d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "from openbb import obb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "154f9ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "obb.user.preferences.output_type = \"dataframe\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d78fbd3",
   "metadata": {},
   "source": [
    "Fetches options chains for the symbol \"SPY\" and stores it in 'chains'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a99c279c",
   "metadata": {},
   "outputs": [],
   "source": [
    "chains = obb.derivatives.options.chains(symbol=\"SPY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e5a2f88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9164 entries, 0 to 9163\n",
      "Data columns (total 27 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   contract_symbol       9164 non-null   object        \n",
      " 1   expiration            9164 non-null   object        \n",
      " 2   strike                9164 non-null   float64       \n",
      " 3   option_type           9164 non-null   object        \n",
      " 4   open_interest         9164 non-null   int64         \n",
      " 5   volume                9164 non-null   int64         \n",
      " 6   theoretical_price     9164 non-null   float64       \n",
      " 7   last_trade_price      9164 non-null   float64       \n",
      " 8   tick                  9164 non-null   object        \n",
      " 9   bid                   9164 non-null   float64       \n",
      " 10  bid_size              9164 non-null   int64         \n",
      " 11  ask                   9164 non-null   float64       \n",
      " 12  ask_size              9164 non-null   int64         \n",
      " 13  open                  9164 non-null   float64       \n",
      " 14  high                  9164 non-null   float64       \n",
      " 15  low                   9164 non-null   float64       \n",
      " 16  prev_close            9164 non-null   float64       \n",
      " 17  change                9164 non-null   float64       \n",
      " 18  change_percent        9164 non-null   float64       \n",
      " 19  implied_volatility    9164 non-null   float64       \n",
      " 20  delta                 9164 non-null   float64       \n",
      " 21  gamma                 9164 non-null   float64       \n",
      " 22  theta                 9164 non-null   float64       \n",
      " 23  vega                  9164 non-null   float64       \n",
      " 24  rho                   9164 non-null   float64       \n",
      " 25  last_trade_timestamp  7566 non-null   datetime64[ns]\n",
      " 26  dte                   9164 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(17), int64(5), object(4)\n",
      "memory usage: 1.9+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(chains.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c6a378",
   "metadata": {},
   "source": [
    "Fetches historical price data for the specified option symbol using the \"yfinance\" provider and selects the \"close\" and \"volume\" columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b185aa90",
   "metadata": {},
   "outputs": [],
   "source": [
    "historic = obb.equity.price.historical(\n",
    "    symbol=\"SPY241220C00550000\", provider=\"yfinance\"\n",
    ")[[\"close\", \"volume\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab4c65de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-15</th>\n",
       "      <td>5.840000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-16</th>\n",
       "      <td>5.630000</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-22</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-23</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26</th>\n",
       "      <td>4.140000</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-10</th>\n",
       "      <td>19.400000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-11</th>\n",
       "      <td>19.950001</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-12</th>\n",
       "      <td>22.540001</td>\n",
       "      <td>1054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-13</th>\n",
       "      <td>22.910000</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-14</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>236 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                close  volume\n",
       "date                         \n",
       "2023-06-15   5.840000       0\n",
       "2023-06-16   5.630000     129\n",
       "2023-06-22   4.600000      16\n",
       "2023-06-23   4.000000       5\n",
       "2023-06-26   4.140000      18\n",
       "...               ...     ...\n",
       "2024-06-10  19.400000       0\n",
       "2024-06-11  19.950001     230\n",
       "2024-06-12  22.540001    1054\n",
       "2024-06-13  22.910000    2008\n",
       "2024-06-14  23.000000    1286\n",
       "\n",
       "[236 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(historic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d104b4e9",
   "metadata": {},
   "source": [
    "Plots the historical \"close\" price data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d12aab32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "historic.close.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c370bac",
   "metadata": {},
   "source": [
    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
  }
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